Explore AI hardware, processors, data center infrastructure, power, cooling, edge computing, and the engineering behind modern artificial intelligence.
Tiny batteries, optical waveguides, AI processors, thermal limits, antennas, cameras, sensors, and advanced semiconductor packaging remain the biggest engineering barriers to replacing today’s smartphone.
As AI GPUs become faster and more power-hungry, the biggest challenge is no longer compute — it’s cooling. Here’s why liquid cooling, thermal management, and next-generation data center design are becoming essential for the future of artificial intelligence.
As AI servers, GPUs, and 112G/224G networking push PCB signal integrity to its limits, selecting the right laminate is no longer optional. Here’s how Megtron 6, Megtron 8, and Tachyon 100G compare in dielectric loss, manufacturability, cost, and real-world AI hardware performance.
As AI models grow larger and data centers consume unprecedented amounts of electricity, neuromorphic computing and brain-inspired processors are emerging as one promising piece of the industry’s response to its looming energy crisis — not a silver bullet, but a real lead worth following.
Edge AI chips are pulling artificial intelligence out of distant data centers and into the devices around you — compact processors, edge computing hardware, TinyML microcontrollers, and next-generation circuit boards making real-time intelligence possible anywhere.
The explosive growth of AI data centers is driving a surge in electricity demand, reviving interest in nuclear power as a reliable, low-carbon energy source for the AI era.